"""simple docstring"""





from math import pi, sqrt
def 	snake_case__					(			_snake_case				:       int     ):

      """simple docstring"""

      if num <= 0:
            raise ValueError("math domain error"     )
      if num > 171.5:
            raise OverflowError("math range error"     )
      elif num - int(a__     ) not in (0, 0.5):
            raise NotImplementedError("num must be an integer or a half-integer"     )
      elif num == 0.5:
            return sqrt(a__     )
      else:
            return 1.0 if num == 1 else (num - 1) * gamma(num - 1     )
def 	snake_case__					(			):

      """simple docstring"""

      assert gamma(0.5     ) == sqrt(a__     )
      assert gamma(1     ) == 1.0
      assert gamma(2     ) == 1.0


if __name__ == "__main__":
   from doctest import testmod

   testmod()
   A :    Union[str, Any]										=      1.0
   while num:
      A :    Optional[Any]										=      float(input('Gamma of: '))
      print(F"gamma({num}) = {gamma(num)}")
      print('\nEnter 0 to exit...')


import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__snake_case				:      Union[str, Any]   		=			logging.getLogger(__name__)



@dataclass
class        A__(a_							):
						"""simple docstring"""






						_A    :				Optional[float]       					=			field(
						    default=0.0,  metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}							)
						_A    :				bool       					=			field(default=a_,  metadata={'''help''': '''Whether to SortishSamler or not.'''}							)
						_A    :				bool       					=			field(
						    default=a_,  metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}							)
						_A    :				bool       					=			field(default=a_,  metadata={'''help''': '''whether to use adafactor'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(default=a_,  metadata={'''help''': '''Dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[str]       					=			field(
						    default='''linear''',  metadata={'''help''': f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()							)}'''},  )


